{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RUL prediction using random forest\n",
    "\n",
    "In this notebook, we will apply random forest to predict RUL of NASA's turbofan engine dataset FD002. We will use scikit-learn to implement random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(324)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy version:  1.18.5\n",
      "Pandas version:  1.0.5\n",
      "Scikit-learn version:  0.23.1\n"
     ]
    }
   ],
   "source": [
    "print(\"Numpy version: \", np.__version__)\n",
    "print(\"Pandas version: \", pd.__version__)\n",
    "print(\"Scikit-learn version: \", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "We strongly encourage readers to go through the [dataset description and prreprocessing notebook](https://github.com/biswajitsahoo1111/rul_codes_open/blob/master/notebooks/cmapss_notebooks/CMAPSS_data_description_and_preprocessing.ipynb). In that notebook we have explained how data preprocessing functions work with simple examples. In this notebook we will only use those functions. So prior familiarity with these functions is an advantage. Below are the parameters that we will use for data preprocessing:\n",
    "\n",
    "* Degradation model: Piecewise linear\n",
    "* Early RUL: 125\n",
    "* Window length: 1\n",
    "* Shift: 1\n",
    "* Data scaling: No (Tree based methods don't require data scaling)\n",
    "\n",
    "We will calculate two prediction scores on test data. In one case, we will take last 5 examples of test data for engine, calculate their predictions, and finally average those for each engine. In the second case, we will take only the last example of each engine and make predictions. The logic behind taking last 5 examples and averaging their predictions is to make the prediction robust against outliers. Due to some external factor, if our last example happens to be corrupted, its prediction outcome might be far off from the actual one. But if we average predictions from last 5 examples, we will get a more conservative estimate. \n",
    "\n",
    "In the following cell we will show boxplots of each column of training data. That will give us an idea about the values in different columns. If all the values in a column are constant, we drop those columns from our analysis.\n",
    "\n",
    "Readers can download the data from [here](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan). In the following cells, wherever data are read from a folder, readers should change the string to point to the respective folder from their system to run this notebook seamlessly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1512 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"/home/biswajit/data/cmapss_data/train_FD002.txt\", sep= \"\\s+\", header = None)\n",
    "plt.figure(figsize = (16, 21))\n",
    "for i in range(21):\n",
    "    temp_data = train_data.iloc[:,i+5]\n",
    "    plt.subplot(7,3,i+1)\n",
    "    plt.boxplot(temp_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_targets(data_length, early_rul = None):\n",
    "    \"\"\" \n",
    "    Takes datalength and earlyrul as input and \n",
    "    creates target rul.\n",
    "    \"\"\"\n",
    "    if early_rul == None:\n",
    "        return np.arange(data_length-1, -1, -1)\n",
    "    else:\n",
    "        early_rul_duration = data_length - early_rul\n",
    "        if early_rul_duration <= 0:\n",
    "            return np.arange(data_length-1, -1, -1)\n",
    "        else:\n",
    "            return np.append(early_rul*np.ones(shape = (early_rul_duration,)), np.arange(early_rul-1, -1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_input_data_with_targets(input_data, target_data = None, window_length = 1, shift = 1):\n",
    "    \"\"\"Depending on values of window_length and shift, this function generates batchs of data and targets \n",
    "    from input_data and target_data.\n",
    "    \n",
    "    Number of batches = np.floor((len(input_data) - window_length)/shift) + 1\n",
    "    \n",
    "    **We don't check input dimensions uisng exception handling. So readers should be careful while using these\n",
    "    functions. If input data are not of desired dimension, either error occurs or something undesirable is \n",
    "    produced as output.**\n",
    "    \n",
    "    Arguments:\n",
    "        input_data: input data to function (Must be 2 dimensional)\n",
    "        target_data: input rul values (Must be 1D array)s\n",
    "        window_length: window length of data\n",
    "        shift: Distance by which the window moves for next batch. This is closely related to overlap\n",
    "               between data. For example, if window length is 30 and shift is 1, there is an overlap of \n",
    "               29 data points between two consecutive batches.\n",
    "        \n",
    "    \"\"\"\n",
    "    num_batches = np.int(np.floor((len(input_data) - window_length)/shift)) + 1\n",
    "    num_features = input_data.shape[1]\n",
    "    output_data = np.repeat(np.nan, repeats = num_batches * window_length * num_features).reshape(num_batches, window_length,\n",
    "                                                                                                  num_features)\n",
    "    if target_data is None:\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "        return output_data\n",
    "    else:\n",
    "        output_targets = np.repeat(np.nan, repeats = num_batches)\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "            output_targets[batch] = target_data[(shift*batch + (window_length-1))]\n",
    "        return output_data, output_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_test_data(test_data_for_an_engine, window_length, shift, num_test_windows = 1):\n",
    "    \"\"\" This function takes test data for an engine as first input. The next two inputs\n",
    "    window_length and shift are same as other functins. \n",
    "    \n",
    "    Finally it takes num_test_windows as the last input. num_test_windows sets how many examplles we\n",
    "    want from test data (from last). By default it extracts only the last example.\n",
    "    \n",
    "    The function return last examples and number of last examples (a scaler) as output. \n",
    "    We need the second output later. If we are extracting more than 1 last examples, we have to \n",
    "    average their prediction results. The second scaler halps us do just that.\n",
    "    \"\"\"\n",
    "    max_num_test_batches = np.int(np.floor((len(test_data_for_an_engine) - window_length)/shift)) + 1\n",
    "    if max_num_test_batches < num_test_windows:\n",
    "        required_len = (max_num_test_batches -1)* shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, max_num_test_batches\n",
    "    else:\n",
    "        required_len = (num_test_windows - 1) * shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, num_test_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed trianing data shape:  (53443, 1, 21)\n",
      "Processed training ruls shape:  (53443,)\n",
      "Processed test data shape:  (1295, 1, 21)\n",
      "True RUL shape:  (259,)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"/home/biswajit/data/cmapss_data/test_FD002.txt\", sep = \"\\s+\", header = None)\n",
    "true_rul = pd.read_csv('/home/biswajit/data/cmapss_data/RUL_FD002.txt', sep = '\\s+', header = None)\n",
    "\n",
    "window_length = 1\n",
    "shift = 1\n",
    "early_rul = 125            \n",
    "processed_train_data = []\n",
    "processed_train_targets = []\n",
    "\n",
    "# How many test windows to take for each engine. If set to 1 (this is the default), only last window of test data for \n",
    "# each engine are taken. If set to a different number, that many windows from last are taken. \n",
    "# Final output is the average of output of all windows.\n",
    "num_test_windows = 5     \n",
    "processed_test_data = []\n",
    "num_test_windows_list = []\n",
    "\n",
    "columns_to_be_dropped = [0,1,2,3,4]\n",
    "\n",
    "num_machines = np.min([len(train_data[0].unique()), len(test_data[0].unique())])\n",
    "\n",
    "for i in np.arange(1, num_machines + 1):\n",
    "    \n",
    "    temp_train_data = train_data[train_data[0] == i].drop(columns=columns_to_be_dropped).values\n",
    "    temp_test_data = test_data[test_data[0] == i].drop(columns=columns_to_be_dropped).values\n",
    "    \n",
    "    # Verify if data of given window length can be extracted from both training and test data\n",
    "    if (len(temp_test_data) < window_length):\n",
    "        print(\"Test engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "    elif (len(temp_train_data) < window_length):\n",
    "        print(\"Train engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "    \n",
    "    temp_train_targets = process_targets(data_length = temp_train_data.shape[0], early_rul = early_rul)\n",
    "    data_for_a_machine, targets_for_a_machine = process_input_data_with_targets(temp_train_data, temp_train_targets, \n",
    "                                                                                window_length = window_length, shift = shift)\n",
    "    \n",
    "    # Prepare test data\n",
    "    test_data_for_an_engine, num_windows = process_test_data(temp_test_data, window_length = window_length, shift = shift,\n",
    "                                                             num_test_windows = num_test_windows)\n",
    "    \n",
    "    processed_train_data.append(data_for_a_machine)\n",
    "    processed_train_targets.append(targets_for_a_machine)\n",
    "    \n",
    "    processed_test_data.append(test_data_for_an_engine)\n",
    "    num_test_windows_list.append(num_windows)\n",
    "\n",
    "processed_train_data = np.concatenate(processed_train_data)\n",
    "processed_train_targets = np.concatenate(processed_train_targets)\n",
    "processed_test_data = np.concatenate(processed_test_data)\n",
    "true_rul = true_rul[0].values\n",
    "\n",
    "# Shuffle data\n",
    "index = np.random.permutation(len(processed_train_targets))\n",
    "processed_train_data, processed_train_targets = processed_train_data[index], processed_train_targets[index]\n",
    "\n",
    "print(\"Processed trianing data shape: \", processed_train_data.shape)\n",
    "print(\"Processed training ruls shape: \", processed_train_targets.shape)\n",
    "print(\"Processed test data shape: \", processed_test_data.shape)\n",
    "print(\"True RUL shape: \", true_rul.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed train data shape:  (53443, 21)\n",
      "Processed test data shape:  (1295, 21)\n"
     ]
    }
   ],
   "source": [
    "processed_train_data = processed_train_data.reshape(-1, processed_train_data.shape[2])\n",
    "processed_test_data = processed_test_data.reshape(-1, processed_test_data.shape[2])\n",
    "print(\"Processed train data shape: \", processed_train_data.shape)\n",
    "print(\"Processed test data shape: \", processed_test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  28.98106562659893\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(n_estimators= 150, max_features = \"sqrt\",\n",
    "                                 n_jobs = -1, random_state = 32)\n",
    "rf_model.fit(processed_train_data, processed_train_targets)\n",
    "rul_pred = rf_model.predict(processed_test_data)\n",
    "\n",
    "# First split predictions according to number of windows of each engine\n",
    "preds_for_each_engine = np.split(rul_pred, np.cumsum(num_test_windows_list)[:-1])\n",
    "mean_pred_for_each_engine = [np.average(ruls_for_each_engine, weights = np.repeat(1/num_windows, num_windows)) \n",
    "                             for ruls_for_each_engine, num_windows in zip(preds_for_each_engine, num_test_windows_list)]\n",
    "RMSE = np.sqrt(mean_squared_error(true_rul, mean_pred_for_each_engine))\n",
    "print(\"RMSE: \", RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With somewhat arbitrary choice of hyperparameters, we obtained an RMSE value of 28.98. But can we do better? To find out better values of hyperparameters, we can do a principled hyperparameter tuning. There are different ways to do hyperparameter search. Here, we will use simple grid search. \n",
    "\n",
    "In grid search, we first define a grid of parameters. Then for each set of parameters, we will fit a 10 xgboost models (as it is 10 fold cross validation). Finally we will average the result of all folds for a particular parameter choice. The parameter choice that gives best score for cross validation is chosen as the best hyperparameter.\n",
    "\n",
    "Grid search of hyperparameters (with cross validation) is computationally intensive. It might take a long time on a personal computer. If that is the case, readers are advised to comment the next cell and directly use the best hyperparameter values as done in subsequent cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [100, 150, 200, 250, 300, 350]},\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\"n_estimators\": [100, 150, 200, 250, 300, 350],\n",
    "              \"max_features\": [\"auto\", \"sqrt\", \"log2\"]}\n",
    "grid = GridSearchCV(RandomForestRegressor(), param_grid = param_grid,scoring = \"neg_root_mean_squared_error\",\n",
    "                    n_jobs = -1, cv = 10)\n",
    "grid.fit(processed_train_data, processed_train_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 'sqrt', 'n_estimators': 350}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the best model to predict on test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE after hyperparameter tuning:  29.000957988388375\n"
     ]
    }
   ],
   "source": [
    "best_rf_model = grid.best_estimator_\n",
    "rul_pred_tuned = best_rf_model.predict(processed_test_data)\n",
    "\n",
    "preds_for_each_engine_tuned = np.split(rul_pred_tuned, np.cumsum(num_test_windows_list)[:-1])\n",
    "mean_pred_for_each_engine_tuned = [np.average(ruls_for_each_engine, weights = np.repeat(1/num_windows, num_windows)) \n",
    "                                   for ruls_for_each_engine, num_windows in zip(preds_for_each_engine_tuned,\n",
    "                                                                                num_test_windows_list)]\n",
    "RMSE_tuned = np.sqrt(mean_squared_error(true_rul, mean_pred_for_each_engine_tuned))\n",
    "print(\"RMSE after hyperparameter tuning: \", RMSE_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that while prediction, we are predicting RUL values for last 5 examples of every engine. Then we take mean of all 5 predictions for each engine and calculate final RMSE.\n",
    "\n",
    "If instead we wish to take only the last example of every engine to make predictions and calculate RUL, we can do so by taking the last prediction of every engine as calculated before and calculate RMSE as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Taking only last examples):  29.15624886984115\n"
     ]
    }
   ],
   "source": [
    "indices_of_last_examples = np.cumsum(num_test_windows_list) - 1\n",
    "preds_for_last_example = np.concatenate(preds_for_each_engine_tuned)[indices_of_last_examples]\n",
    "\n",
    "RMSE_new = np.sqrt(mean_squared_error(true_rul, preds_for_last_example))\n",
    "print(\"RMSE (Taking only last examples): \", RMSE_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are not convinced by above calculations, take a look at the last section of [this notebook](https://github.com/biswajitsahoo1111/rul_codes_open/blob/master/notebooks/cmapss_notebooks/CMAPSS_FD001_xgboost_piecewise_linear_degradation_model.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For CMAPSS data, along with RMSE another metric (S-score) is usually reported in literature. S-score is defined as:\n",
    "\n",
    "$$S= \\sum_{i=1}^N{s_i}$$\n",
    "\n",
    "where, \n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    s_i=\n",
    "    \\begin{cases}\n",
    "      (e^{-\\frac{d_i}{13}})-1, & \\text{for}\\ d_i < 1 \\\\\n",
    "      (e^{\\frac{d_i}{10}})-1, & \\text{for}\\ d_i \\geq 1\\\\\n",
    "    \\end{cases}\n",
    "  \\end{equation}\n",
    "  $$\n",
    "  \n",
    "We can compute the S-metric as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_s_score(rul_true, rul_pred):\n",
    "    \"\"\"\n",
    "    Both rul_true and rul_pred should be 1D numpy arrays.\n",
    "    \"\"\"\n",
    "    diff = rul_pred - rul_true\n",
    "    return np.sum(np.where(diff < 0, np.exp(-diff/13)-1, np.exp(diff/10)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-score:  11764.195597693772\n"
     ]
    }
   ],
   "source": [
    "s_score = compute_s_score(true_rul, preds_for_last_example)\n",
    "print(\"S-score: \", s_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true and predicted RUL values\n",
    "plt.plot(true_rul, label = \"True RUL\", color = \"red\")\n",
    "plt.plot(preds_for_last_example, label = \"Pred RUL\", color = \"blue\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot variable importance score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = best_rf_model.feature_importances_\n",
    "indices = np.argsort(feature_importance)[::-1]\n",
    "num_features = processed_train_data.shape[1]\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(num_features), feature_importance[indices],\n",
    "        color=\"blue\", align=\"center\")\n",
    "plt.xticks(range(num_features), indices)\n",
    "plt.xlim([-1, num_features])\n",
    "plt.xlabel(\"Feature Columns\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final note remember that hyperparameter tuning is more of an art than science. It is possible to obtain better results than what has been obtained here by choosing better set of hyperparameters.\n",
    "\n",
    "For other reproducible results on RUL, interested readers can visit my [project page](https://biswajitsahoo1111.github.io/rul_codes_open). "
   ]
  }
 ],
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